"We have designed our weather and climate foundation model to go beyond such limitations so that it can be tuned to a variety of inputs and uses," said Juan Bernabe-Moreno, emphasizing the flexibility of Prithvi WxC.
"Despite the model's diminutive size, researchers say it was still able to accurately generate global surface temperatures using a random sample that contained just 5 percent of the original data," highlighting its efficiency and performance.
"The model's real advantage may just be its flexibility," indicating that its adaptability for various forecasting scenarios sets it apart from traditional models.
"This space has seen the emergence of large AI models that focus on a fixed dataset and single use case - primarily forecasting," which contrasts with the foundation model developed by IBM and NASA.
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